knitr::opts_chunk$set(
warning = FALSE, # show warnings during codebook generation
message = FALSE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
pander::panderOptions("table.split.table", Inf)
We collected the following data.
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = TRUE, # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)
# Does your dataset have a name that is not reflected in the file name?
# Uncomment the line below and change the name
# metadata(codebook_data)$name <- "My Awesome Dataset"
codebook(codebook_data)
Dataset name: codebook_data
The dataset has N=123 rows and 3 columns. 123 rows have no missing values on any column.
Metadata for search engines
|
#Variables
1
Distribution of values for Geschlecht
0 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.spss | display_width | label |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Geschlecht | numeric | 0 | 1 | 1 | 1 | 2 | 1.195122 | 0.3979154 | ▇▁▁▁▂ | F8.0 | 13 | NA |
| name | value |
|---|---|
| weiblich | 1 |
| maennlich | 2 |
1
Distribution of values for Bildungsniveau
0 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.spss | display_width | label |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Bildungsniveau | numeric | 0 | 1 | 1 | 1 | 2 | 1.00813 | 0.090167 | ▇▁▁▁▁ | F8.0 | 13 | NA |
| name | value |
|---|---|
| Abitur | 1 |
| Kein_Abitur | 2 |
Distribution of values for Alter
0 missing values.
| name | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist | format.spss | display_width | label |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alter | numeric | 0 | 1 | 19 | 22 | 49 | 24.78049 | 6.605433 | ▇▁▁▁▁ | F8.0 | 11 | NA |
JSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "codebook_data",
"datePublished": "2025-05-09",
"description": "The dataset has N=123 rows and 3 columns.\n123 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:--------------|:-----|---------:|\n|Geschlecht |NA | 0|\n|Bildungsniveau |NA | 0|\n|Alter |NA | 0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"keywords": ["Geschlecht", "Bildungsniveau", "Alter"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "Geschlecht",
"value": "1. weiblich,\n2. maennlich",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "Bildungsniveau",
"value": "1. Abitur,\n2. Kein_Abitur",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "Alter",
"@type": "propertyValue"
}
]
}`